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Title: Particle-based fast jet simulation at the LHC with variational autoencoders
Abstract

We study how to use deep variational autoencoders (VAEs) for a fast simulation of jets of particles at the Large Hadron Collider. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a deep VAE to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.

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NSF-PAR ID:
10369061
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
IOP Publishing
Date Published:
Journal Name:
Machine Learning: Science and Technology
Volume:
3
Issue:
3
ISSN:
2632-2153
Page Range / eLocation ID:
Article No. 035003
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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